Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations129971
Missing cells127
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 MiB
Average record size in memory104.0 B

Variable types

Numeric3
Categorical3
Text7

Alerts

country is highly overall correlated with taster_name and 1 other fieldsHigh correlation
points is highly overall correlated with priceHigh correlation
price is highly overall correlated with pointsHigh correlation
taster_name is highly overall correlated with country and 1 other fieldsHigh correlation
taster_twitter_handle is highly overall correlated with country and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2024-12-15 12:34:44.656695
Analysis finished2024-12-15 12:34:57.570603
Duration12.91 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct129971
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64985
Minimum0
Maximum129970
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:34:57.734639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6498.5
Q132492.5
median64985
Q397477.5
95-th percentile123471.5
Maximum129970
Range129970
Interquartile range (IQR)64985

Descriptive statistics

Standard deviation37519.54
Coefficient of variation (CV)0.57735693
Kurtosis-1.2
Mean64985
Median Absolute Deviation (MAD)32493
Skewness0
Sum8.4461654 × 109
Variance1.4077159 × 109
MonotonicityStrictly increasing
2024-12-15T15:34:57.876672image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
86642 1
 
< 0.1%
86655 1
 
< 0.1%
86654 1
 
< 0.1%
86653 1
 
< 0.1%
86652 1
 
< 0.1%
86651 1
 
< 0.1%
86650 1
 
< 0.1%
86649 1
 
< 0.1%
86648 1
 
< 0.1%
Other values (129961) 129961
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
129970 1
< 0.1%
129969 1
< 0.1%
129968 1
< 0.1%
129967 1
< 0.1%
129966 1
< 0.1%
129965 1
< 0.1%
129964 1
< 0.1%
129963 1
< 0.1%
129962 1
< 0.1%
129961 1
< 0.1%

country
Categorical

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing63
Missing (%)< 0.1%
Memory size1015.5 KiB
US
54504 
France
22093 
Italy
19540 
Spain
6645 
Portugal
5691 
Other values (38)
21435 

Length

Max length22
Median length14
Mean length4.4873218
Min length2

Characters and Unicode

Total characters582939
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowItaly
2nd rowPortugal
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 54504
41.9%
France 22093
17.0%
Italy 19540
 
15.0%
Spain 6645
 
5.1%
Portugal 5691
 
4.4%
Chile 4472
 
3.4%
Argentina 3800
 
2.9%
Austria 3345
 
2.6%
Australia 2329
 
1.8%
Germany 2165
 
1.7%
Other values (33) 5324
 
4.1%

Length

2024-12-15T15:34:58.015703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 54504
41.1%
france 22093
16.6%
italy 19540
 
14.7%
spain 6645
 
5.0%
portugal 5691
 
4.3%
chile 4472
 
3.4%
argentina 3800
 
2.9%
austria 3345
 
2.5%
australia 2329
 
1.8%
germany 2165
 
1.6%
Other values (38) 8160
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a 74845
12.8%
S 62657
10.7%
U 54627
 
9.4%
r 42590
 
7.3%
n 40801
 
7.0%
e 37736
 
6.5%
t 36187
 
6.2%
l 34389
 
5.9%
c 24122
 
4.1%
i 22695
 
3.9%
Other values (32) 152290
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 582939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 74845
12.8%
S 62657
10.7%
U 54627
 
9.4%
r 42590
 
7.3%
n 40801
 
7.0%
e 37736
 
6.5%
t 36187
 
6.2%
l 34389
 
5.9%
c 24122
 
4.1%
i 22695
 
3.9%
Other values (32) 152290
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 582939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 74845
12.8%
S 62657
10.7%
U 54627
 
9.4%
r 42590
 
7.3%
n 40801
 
7.0%
e 37736
 
6.5%
t 36187
 
6.2%
l 34389
 
5.9%
c 24122
 
4.1%
i 22695
 
3.9%
Other values (32) 152290
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 582939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 74845
12.8%
S 62657
10.7%
U 54627
 
9.4%
r 42590
 
7.3%
n 40801
 
7.0%
e 37736
 
6.5%
t 36187
 
6.2%
l 34389
 
5.9%
c 24122
 
4.1%
i 22695
 
3.9%
Other values (32) 152290
26.1%
Distinct119955
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:34:58.336894image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length829
Median length533
Mean length242.60106
Min length20

Characters and Unicode

Total characters31531103
Distinct characters143
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109945 ?
Unique (%)84.6%

Sample

1st rowAromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering unripened apple, citrus and dried sage alongside brisk acidity.
2nd rowThis is ripe and fruity, a wine that is smooth while still structured. Firm tannins are filled out with juicy red berry fruits and freshened with acidity. It's already drinkable, although it will certainly be better from 2016.
3rd rowTart and snappy, the flavors of lime flesh and rind dominate. Some green pineapple pokes through, with crisp acidity underscoring the flavors. The wine was all stainless-steel fermented.
4th rowPineapple rind, lemon pith and orange blossom start off the aromas. The palate is a bit more opulent, with notes of honey-drizzled guava and mango giving way to a slightly astringent, semidry finish.
5th rowMuch like the regular bottling from 2012, this comes across as rather rough and tannic, with rustic, earthy, herbal characteristics. Nonetheless, if you think of it as a pleasantly unfussy country wine, it's a good companion to a hearty winter stew.
ValueCountFrequency (%)
and 347400
 
6.6%
the 220934
 
4.2%
a 179461
 
3.4%
of 172926
 
3.3%
with 120408
 
2.3%
this 114015
 
2.2%
is 96715
 
1.8%
wine 78015
 
1.5%
flavors 62678
 
1.2%
in 62322
 
1.2%
Other values (48107) 3793305
72.3%
2024-12-15T15:34:58.831084image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5118835
16.2%
e 2679488
 
8.5%
a 2197359
 
7.0%
t 2037048
 
6.5%
i 1996023
 
6.3%
n 1802979
 
5.7%
r 1761599
 
5.6%
s 1654735
 
5.2%
o 1553393
 
4.9%
l 1186824
 
3.8%
Other values (133) 9542820
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31531103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5118835
16.2%
e 2679488
 
8.5%
a 2197359
 
7.0%
t 2037048
 
6.5%
i 1996023
 
6.3%
n 1802979
 
5.7%
r 1761599
 
5.6%
s 1654735
 
5.2%
o 1553393
 
4.9%
l 1186824
 
3.8%
Other values (133) 9542820
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31531103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5118835
16.2%
e 2679488
 
8.5%
a 2197359
 
7.0%
t 2037048
 
6.5%
i 1996023
 
6.3%
n 1802979
 
5.7%
r 1761599
 
5.6%
s 1654735
 
5.2%
o 1553393
 
4.9%
l 1186824
 
3.8%
Other values (133) 9542820
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31531103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5118835
16.2%
e 2679488
 
8.5%
a 2197359
 
7.0%
t 2037048
 
6.5%
i 1996023
 
6.3%
n 1802979
 
5.7%
r 1761599
 
5.6%
s 1654735
 
5.2%
o 1553393
 
4.9%
l 1186824
 
3.8%
Other values (133) 9542820
30.3%
Distinct37980
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:34:59.145154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length95
Median length82
Mean length12.818098
Min length1

Characters and Unicode

Total characters1665981
Distinct characters149
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22843 ?
Unique (%)17.6%

Sample

1st rowVulkà Bianco
2nd rowAvidagos
3rd rowunknown
4th rowReserve Late Harvest
5th rowVintner's Reserve Wild Child Block
ValueCountFrequency (%)
unknown 37467
 
14.9%
vineyard 12161
 
4.8%
reserve 5279
 
2.1%
estate 5205
 
2.1%
de 4337
 
1.7%
reserva 3752
 
1.5%
brut 3617
 
1.4%
cuvée 2301
 
0.9%
cru 2263
 
0.9%
la 2205
 
0.9%
Other values (24060) 173399
68.8%
2024-12-15T15:34:59.612507image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 193549
 
11.6%
e 166794
 
10.0%
122053
 
7.3%
a 115987
 
7.0%
o 104811
 
6.3%
r 103639
 
6.2%
i 89178
 
5.4%
u 69412
 
4.2%
s 66595
 
4.0%
t 60530
 
3.6%
Other values (139) 573433
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1665981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 193549
 
11.6%
e 166794
 
10.0%
122053
 
7.3%
a 115987
 
7.0%
o 104811
 
6.3%
r 103639
 
6.2%
i 89178
 
5.4%
u 69412
 
4.2%
s 66595
 
4.0%
t 60530
 
3.6%
Other values (139) 573433
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1665981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 193549
 
11.6%
e 166794
 
10.0%
122053
 
7.3%
a 115987
 
7.0%
o 104811
 
6.3%
r 103639
 
6.2%
i 89178
 
5.4%
u 69412
 
4.2%
s 66595
 
4.0%
t 60530
 
3.6%
Other values (139) 573433
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1665981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 193549
 
11.6%
e 166794
 
10.0%
122053
 
7.3%
a 115987
 
7.0%
o 104811
 
6.3%
r 103639
 
6.2%
i 89178
 
5.4%
u 69412
 
4.2%
s 66595
 
4.0%
t 60530
 
3.6%
Other values (139) 573433
34.4%

points
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.447138
Minimum80
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:34:59.734534image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile84
Q186
median88
Q391
95-th percentile93
Maximum100
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0397302
Coefficient of variation (CV)0.034367762
Kurtosis-0.29596319
Mean88.447138
Median Absolute Deviation (MAD)2
Skewness0.045920752
Sum11495563
Variance9.2399597
MonotonicityNot monotonic
2024-12-15T15:34:59.849559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
88 17207
13.2%
87 16933
13.0%
90 15410
11.9%
86 12600
9.7%
89 12226
9.4%
91 11359
8.7%
92 9613
7.4%
85 9530
7.3%
93 6489
 
5.0%
84 6480
 
5.0%
Other values (11) 12124
9.3%
ValueCountFrequency (%)
80 397
 
0.3%
81 692
 
0.5%
82 1836
 
1.4%
83 3025
 
2.3%
84 6480
 
5.0%
85 9530
7.3%
86 12600
9.7%
87 16933
13.0%
88 17207
13.2%
89 12226
9.4%
ValueCountFrequency (%)
100 19
 
< 0.1%
99 33
 
< 0.1%
98 77
 
0.1%
97 229
 
0.2%
96 523
 
0.4%
95 1535
 
1.2%
94 3758
 
2.9%
93 6489
5.0%
92 9613
7.4%
91 11359
8.7%

price
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.363389
Minimum4
Maximum3300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:34:59.972588image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q118
median28
Q340
95-th percentile80
Maximum3300
Range3296
Interquartile range (IQR)22

Descriptive statistics

Standard deviation39.577066
Coefficient of variation (CV)1.1191537
Kurtosis891.42589
Mean35.363389
Median Absolute Deviation (MAD)11
Skewness18.658239
Sum4596215
Variance1566.3442
MonotonicityNot monotonic
2024-12-15T15:35:00.096615image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.36338913 8996
 
6.9%
20 6940
 
5.3%
15 6066
 
4.7%
25 5805
 
4.5%
30 4951
 
3.8%
18 4883
 
3.8%
12 3934
 
3.0%
40 3872
 
3.0%
35 3801
 
2.9%
13 3549
 
2.7%
Other values (381) 77174
59.4%
ValueCountFrequency (%)
4 11
 
< 0.1%
5 46
 
< 0.1%
6 120
 
0.1%
7 433
 
0.3%
8 892
 
0.7%
9 1339
 
1.0%
10 3439
2.6%
11 2058
1.6%
12 3934
3.0%
13 3549
2.7%
ValueCountFrequency (%)
3300 1
< 0.1%
2500 2
< 0.1%
2013 1
< 0.1%
2000 2
< 0.1%
1900 1
< 0.1%
1500 2
< 0.1%
1300 1
< 0.1%
1200 1
< 0.1%
1125 1
< 0.1%
1100 2
< 0.1%
Distinct425
Distinct (%)0.3%
Missing63
Missing (%)< 0.1%
Memory size1015.5 KiB
2024-12-15T15:35:00.365868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length31
Median length29
Mean length9.9764526
Min length3

Characters and Unicode

Total characters1296021
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowSicily & Sardinia
2nd rowDouro
3rd rowOregon
4th rowMichigan
5th rowOregon
ValueCountFrequency (%)
california 36247
22.2%
washington 8639
 
5.3%
valley 7324
 
4.5%
bordeaux 5941
 
3.6%
tuscany 5897
 
3.6%
oregon 5373
 
3.3%
italy 4868
 
3.0%
spain 4400
 
2.7%
burgundy 3980
 
2.4%
northern 3853
 
2.4%
Other values (462) 76723
47.0%
2024-12-15T15:35:00.769261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 162598
12.5%
i 118925
 
9.2%
n 118569
 
9.1%
o 105384
 
8.1%
r 97693
 
7.5%
e 86003
 
6.6%
l 79804
 
6.2%
t 48631
 
3.8%
C 44241
 
3.4%
f 36466
 
2.8%
Other values (63) 397707
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1296021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 162598
12.5%
i 118925
 
9.2%
n 118569
 
9.1%
o 105384
 
8.1%
r 97693
 
7.5%
e 86003
 
6.6%
l 79804
 
6.2%
t 48631
 
3.8%
C 44241
 
3.4%
f 36466
 
2.8%
Other values (63) 397707
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1296021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 162598
12.5%
i 118925
 
9.2%
n 118569
 
9.1%
o 105384
 
8.1%
r 97693
 
7.5%
e 86003
 
6.6%
l 79804
 
6.2%
t 48631
 
3.8%
C 44241
 
3.4%
f 36466
 
2.8%
Other values (63) 397707
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1296021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 162598
12.5%
i 118925
 
9.2%
n 118569
 
9.1%
o 105384
 
8.1%
r 97693
 
7.5%
e 86003
 
6.6%
l 79804
 
6.2%
t 48631
 
3.8%
C 44241
 
3.4%
f 36466
 
2.8%
Other values (63) 397707
30.7%
Distinct1230
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:35:01.012513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length50
Median length42
Mean length12.447731
Min length3

Characters and Unicode

Total characters1617844
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)0.1%

Sample

1st rowEtna
2nd rowunknown
3rd rowWillamette Valley
4th rowLake Michigan Shore
5th rowWillamette Valley
ValueCountFrequency (%)
valley 24944
 
10.5%
unknown 21247
 
9.0%
wa 5276
 
2.2%
de 4638
 
2.0%
napa 4594
 
1.9%
county 4507
 
1.9%
columbia 4363
 
1.8%
santa 4177
 
1.8%
di 3695
 
1.6%
sonoma 3613
 
1.5%
Other values (1237) 155983
65.8%
2024-12-15T15:35:01.403601image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 173210
 
10.7%
n 147104
 
9.1%
l 126465
 
7.8%
e 125233
 
7.7%
o 123295
 
7.6%
107070
 
6.6%
i 86639
 
5.4%
r 66007
 
4.1%
u 61475
 
3.8%
s 55188
 
3.4%
Other values (66) 546158
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1617844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 173210
 
10.7%
n 147104
 
9.1%
l 126465
 
7.8%
e 125233
 
7.7%
o 123295
 
7.6%
107070
 
6.6%
i 86639
 
5.4%
r 66007
 
4.1%
u 61475
 
3.8%
s 55188
 
3.4%
Other values (66) 546158
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1617844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 173210
 
10.7%
n 147104
 
9.1%
l 126465
 
7.8%
e 125233
 
7.7%
o 123295
 
7.6%
107070
 
6.6%
i 86639
 
5.4%
r 66007
 
4.1%
u 61475
 
3.8%
s 55188
 
3.4%
Other values (66) 546158
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1617844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 173210
 
10.7%
n 147104
 
9.1%
l 126465
 
7.8%
e 125233
 
7.7%
o 123295
 
7.6%
107070
 
6.6%
i 86639
 
5.4%
r 66007
 
4.1%
u 61475
 
3.8%
s 55188
 
3.4%
Other values (66) 546158
33.8%

taster_name
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
unknown
26244 
Roger Voss
25514 
Michael Schachner
15134 
Kerin O’Keefe
10776 
Virginie Boone
9537 
Other values (15)
42766 

Length

Max length18
Median length16
Mean length12.005655
Min length7

Characters and Unicode

Total characters1560387
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKerin O’Keefe
2nd rowRoger Voss
3rd rowPaul Gregutt
4th rowAlexander Peartree
5th rowPaul Gregutt

Common Values

ValueCountFrequency (%)
unknown 26244
20.2%
Roger Voss 25514
19.6%
Michael Schachner 15134
11.6%
Kerin O’Keefe 10776
8.3%
Virginie Boone 9537
 
7.3%
Paul Gregutt 9532
 
7.3%
Matt Kettmann 6332
 
4.9%
Joe Czerwinski 5147
 
4.0%
Sean P. Sullivan 4966
 
3.8%
Anna Lee C. Iijima 4415
 
3.4%
Other values (10) 12374
9.5%

Length

2024-12-15T15:35:01.531630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 26244
 
10.4%
voss 25514
 
10.2%
roger 25514
 
10.2%
michael 15134
 
6.0%
schachner 15134
 
6.0%
kerin 10776
 
4.3%
o’keefe 10776
 
4.3%
virginie 9537
 
3.8%
boone 9537
 
3.8%
gregutt 9532
 
3.8%
Other values (33) 93481
37.2%

Most occurring characters

ValueCountFrequency (%)
e 179092
 
11.5%
n 176700
 
11.3%
117523
 
7.5%
o 113308
 
7.3%
r 87957
 
5.6%
i 87685
 
5.6%
a 76266
 
4.9%
s 59499
 
3.8%
u 55029
 
3.5%
h 49093
 
3.1%
Other values (35) 558235
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1560387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 179092
 
11.5%
n 176700
 
11.3%
117523
 
7.5%
o 113308
 
7.3%
r 87957
 
5.6%
i 87685
 
5.6%
a 76266
 
4.9%
s 59499
 
3.8%
u 55029
 
3.5%
h 49093
 
3.1%
Other values (35) 558235
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1560387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 179092
 
11.5%
n 176700
 
11.3%
117523
 
7.5%
o 113308
 
7.3%
r 87957
 
5.6%
i 87685
 
5.6%
a 76266
 
4.9%
s 59499
 
3.8%
u 55029
 
3.5%
h 49093
 
3.1%
Other values (35) 558235
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1560387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 179092
 
11.5%
n 176700
 
11.3%
117523
 
7.5%
o 113308
 
7.3%
r 87957
 
5.6%
i 87685
 
5.6%
a 76266
 
4.9%
s 59499
 
3.8%
u 55029
 
3.5%
h 49093
 
3.1%
Other values (35) 558235
35.8%

taster_twitter_handle
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
unknown
31213 
@vossroger
25514 
@wineschach
15134 
@kerinokeefe
10776 
@vboone
9537 
Other values (11)
37797 

Length

Max length16
Median length15
Mean length9.7802356
Min length6

Characters and Unicode

Total characters1271147
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row@kerinokeefe
2nd row@vossroger
3rd row@paulgwine 
4th rowunknown
5th row@paulgwine 

Common Values

ValueCountFrequency (%)
unknown 31213
24.0%
@vossroger 25514
19.6%
@wineschach 15134
11.6%
@kerinokeefe 10776
 
8.3%
@vboone 9537
 
7.3%
@paulgwine  9532
 
7.3%
@mattkettmann 6332
 
4.9%
@JoeCz 5147
 
4.0%
@wawinereport 4966
 
3.8%
@gordone_cellars 4177
 
3.2%
Other values (6) 7643
 
5.9%

Length

2024-12-15T15:35:01.641655image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 31213
24.0%
vossroger 25514
19.6%
wineschach 15134
11.6%
kerinokeefe 10776
 
8.3%
vboone 9537
 
7.3%
paulgwine 9532
 
7.3%
mattkettmann 6332
 
4.9%
joecz 5147
 
4.0%
wawinereport 4966
 
3.8%
gordone_cellars 4177
 
3.2%
Other values (6) 7643
 
5.9%

Most occurring characters

ValueCountFrequency (%)
n 176209
13.9%
e 138367
10.9%
o 136360
10.7%
@ 98758
 
7.8%
r 84021
 
6.6%
s 74605
 
5.9%
w 68918
 
5.4%
k 60209
 
4.7%
a 49426
 
3.9%
u 46505
 
3.7%
Other values (21) 337769
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1271147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 176209
13.9%
e 138367
10.9%
o 136360
10.7%
@ 98758
 
7.8%
r 84021
 
6.6%
s 74605
 
5.9%
w 68918
 
5.4%
k 60209
 
4.7%
a 49426
 
3.9%
u 46505
 
3.7%
Other values (21) 337769
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1271147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 176209
13.9%
e 138367
10.9%
o 136360
10.7%
@ 98758
 
7.8%
r 84021
 
6.6%
s 74605
 
5.9%
w 68918
 
5.4%
k 60209
 
4.7%
a 49426
 
3.9%
u 46505
 
3.7%
Other values (21) 337769
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1271147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 176209
13.9%
e 138367
10.9%
o 136360
10.7%
@ 98758
 
7.8%
r 84021
 
6.6%
s 74605
 
5.9%
w 68918
 
5.4%
k 60209
 
4.7%
a 49426
 
3.9%
u 46505
 
3.7%
Other values (21) 337769
26.6%

title
Text

Distinct118840
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:35:01.942724image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length136
Median length113
Mean length52.650945
Min length12

Characters and Unicode

Total characters6843096
Distinct characters164
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108104 ?
Unique (%)83.2%

Sample

1st rowNicosia 2013 Vulkà Bianco (Etna)
2nd rowQuinta dos Avidagos 2011 Avidagos Red (Douro)
3rd rowRainstorm 2013 Pinot Gris (Willamette Valley)
4th rowSt. Julian 2013 Reserve Late Harvest Riesling (Lake Michigan Shore)
5th rowSweet Cheeks 2012 Vintner's Reserve Wild Child Block Pinot Noir (Willamette Valley)
ValueCountFrequency (%)
valley 29719
 
3.1%
2013 15875
 
1.6%
2012 15747
 
1.6%
2014 15582
 
1.6%
pinot 15191
 
1.6%
de 15174
 
1.6%
red 15018
 
1.5%
sauvignon 14536
 
1.5%
vineyard 12831
 
1.3%
2011 12558
 
1.3%
Other values (36030) 806932
83.3%
2024-12-15T15:35:02.449286image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
868351
 
12.7%
a 555012
 
8.1%
e 548533
 
8.0%
n 382396
 
5.6%
i 370659
 
5.4%
o 354336
 
5.2%
r 351414
 
5.1%
l 316355
 
4.6%
t 231744
 
3.4%
s 205499
 
3.0%
Other values (154) 2658797
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6843096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
868351
 
12.7%
a 555012
 
8.1%
e 548533
 
8.0%
n 382396
 
5.6%
i 370659
 
5.4%
o 354336
 
5.2%
r 351414
 
5.1%
l 316355
 
4.6%
t 231744
 
3.4%
s 205499
 
3.0%
Other values (154) 2658797
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6843096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
868351
 
12.7%
a 555012
 
8.1%
e 548533
 
8.0%
n 382396
 
5.6%
i 370659
 
5.4%
o 354336
 
5.2%
r 351414
 
5.1%
l 316355
 
4.6%
t 231744
 
3.4%
s 205499
 
3.0%
Other values (154) 2658797
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6843096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
868351
 
12.7%
a 555012
 
8.1%
e 548533
 
8.0%
n 382396
 
5.6%
i 370659
 
5.4%
o 354336
 
5.2%
r 351414
 
5.1%
l 316355
 
4.6%
t 231744
 
3.4%
s 205499
 
3.0%
Other values (154) 2658797
38.9%
Distinct707
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size1015.5 KiB
2024-12-15T15:35:02.737173image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length35
Median length30
Mean length11.668408
Min length4

Characters and Unicode

Total characters1516543
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)0.1%

Sample

1st rowWhite Blend
2nd rowPortuguese Red
3rd rowPinot Gris
4th rowRiesling
5th rowPinot Noir
ValueCountFrequency (%)
blend 25730
 
12.2%
red 19924
 
9.4%
pinot 16644
 
7.9%
sauvignon 15139
 
7.2%
noir 13315
 
6.3%
chardonnay 11755
 
5.6%
cabernet 11346
 
5.4%
bordeaux-style 7981
 
3.8%
blanc 6414
 
3.0%
riesling 5211
 
2.5%
Other values (678) 77560
36.8%
2024-12-15T15:35:03.135265image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 159659
 
10.5%
e 155494
 
10.3%
a 114312
 
7.5%
o 107478
 
7.1%
i 98394
 
6.5%
r 94062
 
6.2%
81053
 
5.3%
l 79158
 
5.2%
d 71640
 
4.7%
t 61798
 
4.1%
Other values (70) 493495
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 159659
 
10.5%
e 155494
 
10.3%
a 114312
 
7.5%
o 107478
 
7.1%
i 98394
 
6.5%
r 94062
 
6.2%
81053
 
5.3%
l 79158
 
5.2%
d 71640
 
4.7%
t 61798
 
4.1%
Other values (70) 493495
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 159659
 
10.5%
e 155494
 
10.3%
a 114312
 
7.5%
o 107478
 
7.1%
i 98394
 
6.5%
r 94062
 
6.2%
81053
 
5.3%
l 79158
 
5.2%
d 71640
 
4.7%
t 61798
 
4.1%
Other values (70) 493495
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 159659
 
10.5%
e 155494
 
10.3%
a 114312
 
7.5%
o 107478
 
7.1%
i 98394
 
6.5%
r 94062
 
6.2%
81053
 
5.3%
l 79158
 
5.2%
d 71640
 
4.7%
t 61798
 
4.1%
Other values (70) 493495
32.5%

winery
Text

Distinct16757
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1015.5 KiB
2024-12-15T15:35:03.459339image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length54
Median length39
Mean length12.308569
Min length1

Characters and Unicode

Total characters1599757
Distinct characters125
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4240 ?
Unique (%)3.3%

Sample

1st rowNicosia
2nd rowQuinta dos Avidagos
3rd rowRainstorm
4th rowSt. Julian
5th rowSweet Cheeks
ValueCountFrequency (%)
château 7322
 
3.0%
de 5930
 
2.4%
domaine 4467
 
1.8%
la 2890
 
1.2%
2157
 
0.9%
cellars 2120
 
0.9%
di 1890
 
0.8%
vineyards 1782
 
0.7%
quinta 1278
 
0.5%
estate 1233
 
0.5%
Other values (16002) 213963
87.3%
2024-12-15T15:35:04.086488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 159830
 
10.0%
e 156178
 
9.8%
115064
 
7.2%
i 106630
 
6.7%
r 100567
 
6.3%
o 99188
 
6.2%
n 96440
 
6.0%
l 80017
 
5.0%
t 71582
 
4.5%
s 62868
 
3.9%
Other values (115) 551393
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 159830
 
10.0%
e 156178
 
9.8%
115064
 
7.2%
i 106630
 
6.7%
r 100567
 
6.3%
o 99188
 
6.2%
n 96440
 
6.0%
l 80017
 
5.0%
t 71582
 
4.5%
s 62868
 
3.9%
Other values (115) 551393
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 159830
 
10.0%
e 156178
 
9.8%
115064
 
7.2%
i 106630
 
6.7%
r 100567
 
6.3%
o 99188
 
6.2%
n 96440
 
6.0%
l 80017
 
5.0%
t 71582
 
4.5%
s 62868
 
3.9%
Other values (115) 551393
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 159830
 
10.0%
e 156178
 
9.8%
115064
 
7.2%
i 106630
 
6.7%
r 100567
 
6.3%
o 99188
 
6.2%
n 96440
 
6.0%
l 80017
 
5.0%
t 71582
 
4.5%
s 62868
 
3.9%
Other values (115) 551393
34.5%

Interactions

2024-12-15T15:34:56.247649image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:55.557187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:55.910343image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:56.365675image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:55.673288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:56.016366image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:56.482703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:55.800317image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-12-15T15:34:56.129622image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-12-15T15:35:04.186511image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Unnamed: 0countrypointspricetaster_nametaster_twitter_handle
Unnamed: 01.0000.0160.0090.0040.0160.015
country0.0161.0000.0920.0130.5680.584
points0.0090.0921.0000.5810.1170.113
price0.0040.0130.5811.0000.0140.014
taster_name0.0160.5680.1170.0141.0001.000
taster_twitter_handle0.0150.5840.1130.0141.0001.000

Missing values

2024-12-15T15:34:56.657823image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T15:34:56.964889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-15T15:34:57.296540image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0countrydescriptiondesignationpointspriceprovinceregion_1taster_nametaster_twitter_handletitlevarietywinery
00ItalyAromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering unripened apple, citrus and dried sage alongside brisk acidity.Vulkà Bianco8735.363389Sicily & SardiniaEtnaKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
11PortugalThis is ripe and fruity, a wine that is smooth while still structured. Firm tannins are filled out with juicy red berry fruits and freshened with acidity. It's already drinkable, although it will certainly be better from 2016.Avidagos8715.000000DourounknownRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
22USTart and snappy, the flavors of lime flesh and rind dominate. Some green pineapple pokes through, with crisp acidity underscoring the flavors. The wine was all stainless-steel fermented.unknown8714.000000OregonWillamette ValleyPaul Gregutt@paulgwineRainstorm 2013 Pinot Gris (Willamette Valley)Pinot GrisRainstorm
33USPineapple rind, lemon pith and orange blossom start off the aromas. The palate is a bit more opulent, with notes of honey-drizzled guava and mango giving way to a slightly astringent, semidry finish.Reserve Late Harvest8713.000000MichiganLake Michigan ShoreAlexander PeartreeunknownSt. Julian 2013 Reserve Late Harvest Riesling (Lake Michigan Shore)RieslingSt. Julian
44USMuch like the regular bottling from 2012, this comes across as rather rough and tannic, with rustic, earthy, herbal characteristics. Nonetheless, if you think of it as a pleasantly unfussy country wine, it's a good companion to a hearty winter stew.Vintner's Reserve Wild Child Block8765.000000OregonWillamette ValleyPaul Gregutt@paulgwineSweet Cheeks 2012 Vintner's Reserve Wild Child Block Pinot Noir (Willamette Valley)Pinot NoirSweet Cheeks
55SpainBlackberry and raspberry aromas show a typical Navarran whiff of green herbs and, in this case, horseradish. In the mouth, this is fairly full bodied, with tomatoey acidity. Spicy, herbal flavors complement dark plum fruit, while the finish is fresh but grabby.Ars In Vitro8715.000000Northern SpainNavarraMichael Schachner@wineschachTandem 2011 Ars In Vitro Tempranillo-Merlot (Navarra)Tempranillo-MerlotTandem
66ItalyHere's a bright, informal red that opens with aromas of candied berry, white pepper and savory herb that carry over to the palate. It's balanced with fresh acidity and soft tannins.Belsito8716.000000Sicily & SardiniaVittoriaKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo
77FranceThis dry and restrained wine offers spice in profusion. Balanced with acidity and a firm texture, it's very much for food.unknown8724.000000AlsaceAlsaceRoger Voss@vossrogerTrimbach 2012 Gewurztraminer (Alsace)GewürztraminerTrimbach
88GermanySavory dried thyme notes accent sunnier flavors of preserved peach in this brisk, off-dry wine. It's fruity and fresh, with an elegant, sprightly footprint.Shine8712.000000RheinhessenunknownAnna Lee C. IijimaunknownHeinz Eifel 2013 Shine Gewürztraminer (Rheinhessen)GewürztraminerHeinz Eifel
99FranceThis has great depth of flavor with its fresh apple and pear fruits and touch of spice. It's off dry while balanced with acidity and a crisp texture. Drink now.Les Natures8727.000000AlsaceAlsaceRoger Voss@vossrogerJean-Baptiste Adam 2012 Les Natures Pinot Gris (Alsace)Pinot GrisJean-Baptiste Adam
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129961129961ItalyIntense aromas of wild cherry, baking spice, tilled soil and savory herb lead the nose on this soulful, silky red. The round, smooth palate doles out juicy red cherry, strawberry jelly, mineral, white pepper and an intriguing note of zabaglione alongside soft, supple tannins and bright acidity..unknown9030.000000Sicily & SardiniaSiciliaKerin O’Keefe@kerinokeefeCOS 2013 Frappato (Sicilia)FrappatoCOS
129962129962ItalyBlackberry, cassis, grilled herb and toasted aromas come together in the glass. On the palate, espresso, mint and black pepper add depth to the core of black cherry and blackberry flavors. It finishes on a licorice note.Sàgana Tenuta San Giacomo9040.000000Sicily & SardiniaSiciliaKerin O’Keefe@kerinokeefeCusumano 2012 Sàgana Tenuta San Giacomo Nero d'Avola (Sicilia)Nero d'AvolaCusumano
129963129963IsraelA bouquet of black cherry, tart cranberry and clove opens into flavors of cherry, anisette, espresso bean and mint, with a hint of tart cranberry. The minty notes can almost seem overly strong for a moment, but tart tones bring the fruit flavors back to the foreground. The pleasantly gripping tannins will mellow with a few more years of aging.Oak Aged9020.000000GalileeunknownMike DeSimone@worldwineguysDalton 2012 Oak Aged Cabernet Sauvignon (Galilee)Cabernet SauvignonDalton
129964129964FranceInitially quite muted, this wine slowly develops impressive richness and spice. It's not sweet, more medium dry, with the spice forming a core of dryness that contrasts with the honeyed texture. It can develop more, so wait to drink until 2016.Domaine Saint-Rémy Herrenweg9035.363389AlsaceAlsaceRoger Voss@vossrogerDomaine Ehrhart 2013 Domaine Saint-Rémy Herrenweg Gewurztraminer (Alsace)GewürztraminerDomaine Ehrhart
129965129965FranceWhile it's rich, this beautiful dry wine also offers considerable freshness. Acidity cuts easily through the ripe white fruit, pear and red apples, allowing room for spice that provides a contrasting aftertaste.Seppi Landmann Vallée Noble9028.000000AlsaceAlsaceRoger Voss@vossrogerDomaine Rieflé-Landmann 2013 Seppi Landmann Vallée Noble Pinot Gris (Alsace)Pinot GrisDomaine Rieflé-Landmann
129966129966GermanyNotes of honeysuckle and cantaloupe sweeten this deliciously feather-light spätlese. It's intensely juicy, quenching the palate with streams of tart tangerine and grapefruit acidity, yet wraps up with a kiss of honey and peach.Brauneberger Juffer-Sonnenuhr Spätlese9028.000000MoselunknownAnna Lee C. IijimaunknownDr. H. Thanisch (Erben Müller-Burggraef) 2013 Brauneberger Juffer-Sonnenuhr Spätlese Riesling (Mosel)RieslingDr. H. Thanisch (Erben Müller-Burggraef)
129967129967USCitation is given as much as a decade of bottle age prior to release, which means it is pre-cellared and drinking at its peak. Baked cherry, cocoa and coconut flavors combine gracefully, with soft, secondary fruit compote highlights.unknown9075.000000OregonOregonPaul Gregutt@paulgwineCitation 2004 Pinot Noir (Oregon)Pinot NoirCitation
129968129968FranceWell-drained gravel soil gives this wine its crisp and dry character. It is ripe and fruity, although the spice is subdued in favor of a more serious structure. This is a wine to age for a couple of years, so drink from 2017.Kritt9030.000000AlsaceAlsaceRoger Voss@vossrogerDomaine Gresser 2013 Kritt Gewurztraminer (Alsace)GewürztraminerDomaine Gresser
129969129969FranceA dry style of Pinot Gris, this is crisp with some acidity. It also has weight and a solid, powerful core of spice and baked apple flavors. With its structure still developing, the wine needs to age. Drink from 2015.unknown9032.000000AlsaceAlsaceRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970129970FranceBig, rich and off-dry, this is powered by intense spiciness and rounded texture. Lychees dominate the fruit profile, giving an opulent feel to the aftertaste. Drink now.Lieu-dit Harth Cuvée Caroline9021.000000AlsaceAlsaceRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Caroline Gewurztraminer (Alsace)GewürztraminerDomaine Schoffit